Post-disaster damage mapping is an essential task following tragic events such as hurricanes, earthquakes, and tsunamis. It is also a time-consuming and risky task that still often requires the sending of experts on the ground to meticulously map and assess the damages. Presently, the increasing number of remote-sensing satellites taking pictures of Earth on a regular basis with programs such as Sentinel, ASTER, or Landsat makes it easy to acquire almost in real time images from areas struck by a disaster before and after it hits. While the manual study of such images is also a tedious task, progress in artificial intelligence and in particular deep-learning techniques makes it possible to analyze such images to quickly detect areas that have been flooded or destroyed. From there, it is possible to evaluate both the extent and the severity of the damages. In this paper, we present a state-of-the-art deep-learning approach for change detection applied to satellite images taken before and after the Tohoku tsunami of 2011. We compare our approach with other machine-learning methods and show that our approach is superior to existing techniques due to its unsupervised nature, good performance, and relative speed of analysis.

Unsupervised machine learning approa ches involving several clustering algorithms working together to tackle difficult data sets are a recent area of research with a large number of applications such as clustering of distributed data, multi-expert clustering, multi-scale clustering analysis or multi-view clustering. Most of these frameworks can be regrouped under the umbrella of collaborative clustering, the aim of which is to reveal the common underlying structures found by the different algorithms while analyzing the data. Within this context, the purpose of this article is to propose a collaborative framework lifting the limitations of many of the previously proposed methods: Our proposed collaborative learning method makes possible for a wide range of clustering algorithms from different families to work together based solely on their clustering solutions, thus lifting previous limitation requiring identical prototypes between the different collaborators. Our proposed framework uses a variational EM as its theoretical basis for the collaboration process and can be applied to any of the previously mentioned collaborative contexts. In this article, we give the main ideas and theoretical foundations of our method, and we demonstrate its effectiveness in a series of experiments on real data sets as well as data sets from the literature.

With the growing number of open source satellite image time series, such as SPOT or Sentinel-2, the number of potential change detection applications is increasing every year. However, due to the image quality and resolution, the change detection process is a challenge nowadays. In this work, we propose an approach that uses the reconstruction losses of joint autoencoders to detect non-trivial changes (permanent changes and seasonal changes that do not follow common tendency) between two co-registered images in a satellite image time series. The autoencoder aims to learn a transformation model that reconstructs one co-registered image from another. Since trivial changes such as changes in luminosity or seasonal changes between two dates have a tendency to repeat in different areas of the image, their transformation model can be easily learned. However, non-trivial changes tend to be unique and can not be correctly translated from one date to another, hence an elevated reconstruction error where there is change. In this work, we compare two models in order to find the most performing one. The proposed approach is completely unsupervised and gives promising results for an open source time series when compared with other concurrent methods.

Nowadays, huge volume of satellite images, via the different Earth Observation missions, are constantly acquired and they constitute a valuable source of information for the analysis of spatiotemporal phenomena. However, it can be challenging to obtain reference data associated to such images to deal with land use or land cover changes as often the nature of the phenomena under study is not known a priori. With the aim to deal with satellite image analysis, considering a real-world scenario, where reference data cannot be available, in this article, we present a novel end-to-end unsupervised approach for change detection and clustering for satellite image time series (SITS). In the proposed framework, we first create bitemporal change masks for every couple of consecutive images using neural network autoencoders (AEs). Then, we associate the extracted changes to different spatial objects. The objects sharing the same geographical location are combined in spatiotemporal evolution graphs that are finally clustered accordingly to the type of change process with gated recurrent unit (GRU) AE-based model. The proposed approach was assessed on two real-world SITS data supplying promising results. Index Terms-Autoencoder (AE), change detection, gated recurrent unit (GRU), object-oriented image analysis, pattern recognition, satellite image time series (SITS), time-series clustering, unsupervised learning.

Collaborative clustering is a recent field of Machine Learning that shows similarities with both ensemble learning and transfer learning. Using a two-step approach where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement, collaborative clustering has shown promising performances when trying to have several algorithms working on the same data. However the field is still lagging behind when it comes to transfer learning where several algorithms are working on different data with similar clusters and the same features.In this article, we propose an original method where we combine the topological structure of the Generative Topographic Mapping (GTM) algorithm and take advantage of it to transfer information between collaborating algorithms working on different data sets featuring similar distributions.The proposed approach has been validated on several data sets, and the experimental results have shown very promising performances.

Clustering is a compression task which consists in grouping similar objects into clusters. In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views. In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications. Using this approach, we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets.

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